Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients. (arXiv:2201.10899v1 [cs.LG])

Federated Learning (FL) allows training machine learning models in
privacy-constrained scenarios by enabling the cooperation of edge devices
without requiring local data sharing. This approach raises several challenges
due to the different statistical distribution of the local datasets and the
clients' computational heterogeneity. In particular, the presence of highly
non-i.i.d. data severely impairs both the performance of the trained neural
network and its convergence rate, increasing the number of communication rounds
requested to reach a performance comparable to that of …

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